“Texas in Australia? Imaging channel sands in the Cooper Basin”

Abstract

This paper is inspired by a picture of a time slice from the prolific hydrocarbon state of Texas showing a stacked channel systems. Similar channel systems also exist in Australia's Cooper Basin but often difficult to see with typical sparse seismic acquisition. The desire is to see these channels, as in Texas. Due to reasons of cost and, environmental and cultural heritage protection relatively wide (sparse) line spacing gives the ability to cover larger areas than could otherwise be achieved. These sparse designs can provide a low environmental impact with reasonable images at target. Lack of traces at medium and near offset ranges may result in strong amplitude artifacts in the final image acquisition footprint?. To reduce these artifacts, we deployed sources in a smooth 'wavy'? sinusoidal pattern, modified as necessary to follow natural features in the terrain. This methodology results in acquisition with a minimal visual impact and provides significant benefits in reducing the acquisition footprint. This case study is from the 2012 acquisition. Innovative survey design and coordinate driven data processing techniques resulted in the dataset where channel features are now clearly visible on the migrated volumes. The acquisition technique features broad-band point-sources using a non-linear Maximum Displacement sweep of 2 to 100 Hz, broad-band point-receivers and dense sampling along both the source and receiver lines. In this paper we will demonstrate that even such relatively sparse 3D data can provide good signal-to-noise datasets suitable for extraction of seismic attributes, confirmed by identification of channel geobodies. Using the described technologies the acquired survey not only met, but by far exceeded, the initial expectations, and within the time frame. This survey has shown that exploration seismic surveys can be tailored to minimize environmental and acquisition foot print in Australia, high quality seismic dataset suitable for seimic attributes extraction